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Updated: May 11, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A simulation optimization method for coordination of production, transportation and sales.

Yi Zheng1, Ming Lei1, Yijie Peng1

  • 1Guanghua School of Management, Peking University, Beijing 100871, China.

Fundamental Research
|April 17, 2025
PubMed
Summary

This study optimizes multi-echelon supply chains by coordinating production, transportation, and sales. A novel simulation optimization technique enhances marketing strategies, significantly boosting expected profits and reducing computational load.

Keywords:
Linear programmingMarketing strategyMixed integer linear programmingSimulation optimizationSupply chain coordination

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Computational Optimization

Background:

  • Coordinating production, transportation, and sales in multi-echelon supply chains is complex.
  • Customer demand is dynamic and influenced by marketing strategies.
  • Existing models often struggle with the stochastic nature of demand.

Purpose of the Study:

  • To develop a method for optimizing marketing strategies in supply chains.
  • To maximize the expected profit of a multi-echelon supply chain network.
  • To improve the efficiency of decision-making under uncertain customer demand.

Main Methods:

  • A simulation model was developed to generate random customer demands.
  • The coordination problem was formulated as a linear programming problem.
  • A simulation optimization technique was employed to learn the optimal marketing strategy.

Main Results:

  • The proposed method significantly improved the expected profit of the supply chain.
  • Computational burden was reduced for achieving a desired probability of correct strategy selection.
  • The approach was extended to mixed-integer programming, demonstrating computational efficiency.

Conclusions:

  • Simulation optimization effectively addresses supply chain coordination under random demand.
  • The method offers a robust approach to maximizing supply chain profitability.
  • The technique is computationally efficient for both linear and mixed-integer programming formulations.